{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PHydro-cover-small.png\">\n",
    "*This is the Jupyter notebook version of the [Python in Hydrology](http://www.greenteapress.com/pythonhydro/pythonhydro.html) by Sat Kumar Tomer.*\n",
    "*Source code is available at [code.google.com](https://code.google.com/archive/p/python-in-hydrology/source).*\n",
    "\n",
    "*The book is available under the [GNU Free Documentation License](http://www.gnu.org/copyleft/fdl.html). If you have comments, corrections or suggestions, please send email to satkumartomer@gmail.com.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Plotyy](07.08-Plotyy.ipynb) | [Contents](Index.ipynb) | [Basemap](07.10-Basemap.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.9 注释\n",
    "\n",
    "除了绘制折线、点、图例等之外，我们可能需要在图形上添加额外的信息，这称为注释。我们可以添加不同的箭头、文本等来使我们的图表更为清晰。图7.12显示了一个这样的图形，它具有少量箭头、文本来提高图形的可阅读性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f419ae8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "t = np.linspace(0,10)\n",
    "y = 5*np.sin(t)\n",
    "\n",
    "plt.plot(t,y,lw=3,color='m')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.annotate('ridge',xy=(np.pi/2,5),xycoords='data',\n",
    "            xytext = (-20,-75),textcoords='offset points',\n",
    "            arrowprops=dict(arrowstyle=\"->\")\n",
    "           )\n",
    "\n",
    "plt.annotate('valley', xy=(1.5*np.pi, -5),  xycoords='data',\n",
    "             xytext=(-30, 80), textcoords='offset points',\n",
    "             size=20,\n",
    "             bbox=dict(boxstyle=\"round,pad=.5\",fc=\"0.8\"),\n",
    "             arrowprops=dict(arrowstyle=\"->\",),\n",
    "            )\n",
    "plt.annotate('Annotation', xy=(8, -5),  xycoords='data',\n",
    "             xytext=(-20, 0), textcoords='offset points',\n",
    "             bbox=dict(boxstyle=\"round\", fc=\"green\"), fontsize=15)\n",
    "\n",
    "plt.text(3.0, 0, \"Down\", color = \"w\", ha=\"center\", va=\"center\", rotation=90,\n",
    "        size=15, bbox=dict(boxstyle=\"larrow,pad=0.3\", fc=\"r\", ec=\"r\", lw=2))\n",
    "\n",
    "plt.text(np.pi*2, 0, \"UP\", color = \"w\", ha=\"center\", va=\"center\", rotation=90,\n",
    "        size=15, bbox=dict(boxstyle=\"rarrow,pad=0.3\", fc=\"g\",ec=\"g\",lw=2))\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
